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Strategy Tools
Strategy Tools: The Ansoff Matrix
The Ansoff Matrix is a strategic-planning tool that provides a framework to help executives, senior managers, and marketers devise strategies for future growth. It is named after Russian American Igor Ansoff, who came up with the concept. Ansoff suggested that there were effectively only two approaches to developing a growth strategy; through varying what is sold (product growth) and who it is sold to (market growth).
“When we are in peak, we make a ton of money, as soon as we make a ton of money, we are desperately looking for ways to spend it. And we diversify into areas that, frankly, we don’t know how to run very well,” mused Bill Ford, great grandson of Henry. Ford’s story is neither unique nor new and companies often choose sub-optimal growth paths.
Igor Ansoff created the product / market matrix to illustrate the inherent risks in four generic growth strategies:
- Market penetration / consumption – the firm seeks to achieve growth with existing products in their current market segments, aiming to increase market share.
- Market development – the firm seeks growth by pushing its existing products into new market segments.
- Product development – the firm develops new products targeted to its existing market segments.
- Diversification – the firm grows by developing new products for new markets.
Ansoff’s Matrix
Selecting a Product-Market growth strategy
Market penetration / consumption
Market penetration and consumption covers products that are existent in an existing market. In this strategy, there can be further exploitation of the products without necessarily changing the product or the outlook of the product. This will be possible through the use of promotional methods, putting various pricing policies that may attract more clientele, or one can make the distribution more extensive.
Market penetration or consumption can also be increased is by coming up with various initiatives that will encourage increased usage of the product. A good example is the usage of toothpaste. Research has shown that the toothbrush head influences the amount of toothpaste that one will use. Thus if the head of the toothbrush is bigger it will mean that more toothpaste will be used thus promoting the usage of the toothpaste and eventually leading to more purchase of the toothpaste.
In market penetration / consumption, the risk involved is usually the least since the products are already familiar to the consumers and so is the established market.
Market development
In this strategy, the business sells its existing products to new markets. This can be made possible through further market segmentation to aid in identifying a new clientele base. This strategy assumes that the existing markets have been fully exploited thus the need to venture into new markets. There are various approaches to this strategy, which include: new geographical markets, new distribution channels, new product packaging, and different pricing policies.
Going into new geographies could involve launching the product in a completely different market. A good example is Guinness. This beer had originally been made to be sold in countries that have a colder climate, but now it is also being sold in African countries.
New distribution channels could entail selling the products via e-commerce or mail order. Selling through e-commerce may capture a larger clientele base since we are in a digital era where most people access the internet often. In new product packaging, it means repacking the product in another method or dimension. That way it may attract a different customer base. In different pricing policies, the business could change its prices so as to attract a different customer base or create a new market segment.
Product development
With a product-development growth strategy, a new product is introduced into existing markets. Product development can be from the introduction of a new product in an existing market or it can involve the modification of an existing product. By modifying the product one could change its outlook or presentation, increase the product’s performance or quality. By doing so, it can be more appealing to the existing market. A good example is car manufacturers who offer a range of car parts so as to target the car owners in purchasing additional products.
Diversification
This growth strategy involves an organisation marketing or selling new products to new markets at the same time. It is the most risky strategy as it involves two unknowns:
- New products are being created and the business does not know the development problems that may occur in the process.
- There is also the fact that there is a new market being targeted, which will bring the problem of having unknown characteristics.
For a business to take a step into diversification, they need to have their facts right regarding what it expects to gain from the strategy and have a clear assessment of the risks involved. There are two types of diversification – related diversification and unrelated diversification.
In related diversification, the business remains in the same industry in which it is currently operating. For example, a cake manufacturer diversifies into fresh-juice manufacturing. This diversification is within the food industry.
In unrelated diversification, there are usually no previous industry relations or market experiences. One can diversify from a food industry into the personal-care industry. A good example of the unrelated diversification is Richard Branson. He took advantage of the Virgin brand and diversified into various fields such as entertainment, air and rail travel, foods, etc.
Conclusion
The Ansoff matrix gives managers a framework for surveying all the initiatives the business has under way – how many are being pursued in each realm and how much investment is going to each type, and also allows managers to understand the risks and thus probability of success of each initiative.
To use the tool effectively, a company may take its sales initiatives for the next 3-5 years and place them in each of the quadrants in the matrix and analyse which quadrant shows the greatest uplift in sales. If it is in existing products to existing or new markets, or new products to existing products, there should be no cause for alarm. If it is in the new products to new markets quadrant, then this will require a greater effort at greater risk.
Companies that focus on the three quadrants other than diversification find more success as these strategies are built on familiar skills in production, purchasing, sales and marketing. An HBR study found that companies that invested 70% of their resources in core operations i.e. the market penetration quadrant, out-performed those that did not.
A diversification strategy operates in a higher plane of risk than the other three strategies. Superficially attractive and practiced by many companies, it is distracting and absorbs a disproportionately high proportion of managerial and engineering resources due to the lack of familiarity with the new venture.
Sources
- Evans, V – “25 need-to-know strategy tools” – FT Publishing – 2014
- Anonymous – “Ansoff Matrix” – Strategic Management – Quick MBA – http://www.quickmba.com/strategy/matrix/ansoff/
- Anonymous – “What is the Ansoff matrix?” – http://www.ansoffmatrix.com/
- https://en.wikipedia.org/wiki/Ansoff_Matrix
- Nagji, B; Tuff, G – “Managing Your Innovation Portfolio” – Harvard Business Review – 2012 – https://hbr.org/2012/05/managing-your-innovation-portfolio
Fast Facts
South African retailers have maintained flat margins on lamb and seen declining margins on beef
- Beef producers’ share of retail prices has increased from 43% to 45% from 2000 to 2013 while lamb producers’ share has decreased from 55% to 53%
- Lamb prices have escalated above other meat prices as producers have passed on supplier increases
- Retailers have been unwilling to cushion these increases
- Retailers have cushioned an increase in beef producer prices and taken smaller margins
- Retail prices of beef have risen at a slower rate than producer prices
- Beef consumption is growing with the rise of the middle class while lamb consumption is declining
- Demand for beef is higher than lamb due to affordability
- Retailers are willing to take less margin on beef in order to maintain foot traffic through their stores
Selected News
Quote: Andrej Karpathy – Ex-OpenAI, Ex-Tesla AI
“[With AI] we’re not building animals. We’re building ghosts or spirits.” – Andrej Karpathy – Ex-OpenAI, Ex-Tesla AI
Andrej Karpathy, renowned for his leadership roles at OpenAI and Tesla’s Autopilot programme, has been at the centre of advances in deep learning, neural networks, and applied artificial intelligence. His work traverses both academic research and industrial deployment, granting him a panoramic perspective on the state and direction of AI.
When Karpathy refers to building “ghosts or spirits,” he is drawing a conceptual line between biological intelligence—the product of millions of years of evolution—and artificial intelligence as developed through data-driven, digital systems. In his view, animals are “baked in” with instincts, embodiment, and innate learning capacities shaped by evolution, a process unfolding over geological timeframes. By contrast, today’s AI models are “ghosts” in the sense that they are ethereal, fully digital artefacts, trained to imitate human-generated data rather than to evolve or learn through direct interaction with the physical world. They lack bodily instincts and the evolutionary substrate that endows animals with survival strategies and adaptation mechanisms.
Karpathy describes the pre-training process that underpins large language models as a form of “crappy evolution”—a shortcut that builds digital entities by absorbing the statistical patterns of internet-scale data without the iterative adaptation of embodied beings. Consequently, these models are not “born” into the world like animals with built-in survival machinery; instead, they are bootstrapped as “ghosts,” imitating but not experiencing life.
The Cognitive Core—Karpathy’s Vision for AI Intelligence
Karpathy’s thinking has advanced towards the critical notion of the “cognitive core”: the kernel of intelligence responsible for reasoning, abstraction, and problem-solving, abstracted away from encyclopaedic factual knowledge. He argues that the true magic of intelligence is not in the passive recall of data, but in the flexible, generalisable ability to manipulate ideas, solve problems, and intuit patterns—capabilities that a system exhibits even when deprived of pre-programmed facts or exhaustive memory.
He warns against confusing memorisation (the stockpiling of internet facts within a model) with general intelligence, which arises from this cognitive core. The most promising path, in his view, is to isolate and refine this core, stripping away the accretions of memorised data, thereby developing something akin to a “ghost” of reasoning and abstraction rather than an “animal” shaped by instinct and inheritance.
This approach entails significant trade-offs: a cognitive core lacks the encyclopaedic reach of today’s massive models, but gains in adaptability, transparency, and the capacity for compositional, creative thought. By foregrounding reasoning machinery, Karpathy posits that AI can begin to mirror not the inflexibility of animals, but the open-ended, reflective qualities that characterise high-level problem-solving.
Karpathy’s Journey and Influence
Karpathy’s influence is rooted in a career spent on the frontier of AI research and deployment. His early proximity to Geoffrey Hinton at the University of Toronto placed him at the launch-point of the convolutional neural networks revolution, which fundamentally reshaped computer vision and pattern recognition.
At OpenAI, Karpathy contributed to an early focus on training agents to master digital environments (such as Atari games), a direction in retrospect he now considers premature. He found greater promise in systems that could interact with the digital world through knowledge work—precursors to today’s agentic models—a vision he is now helping to realise through ongoing work in educational technology and AI deployment.
Later, at Tesla, he directed the transformation of autonomous vehicles from demonstration to product, gaining hard-won appreciation for the “march of nines”—the reality that progressing from system prototypes that work 90% of the time to those that work 99.999% of the time requires exponentially more effort. This experience informs his scepticism towards aggressive timelines for “AGI” and his insistence on the qualitative differences between robust system deployment and controlled demonstrations.
The Leading Theorists Shaping the Debate
Karpathy’s conceptual framework emerges amid vibrant discourse within the AI community, shaped by several seminal thinkers:
Sutton’s “bitter lesson” posits that scale and generic algorithms, rather than domain-specific tricks, ultimately win—suggesting a focus on evolving animal-like intelligence. Karpathy, however, notes that current development practices, with their reliance on dataset imitation, sidestep the deep embodiment and evolutionary learning that define animal cognition. Instead, AI today creates digital ghosts—entities whose minds are not grounded in physical reality, but in the manifold of internet text and data.
Hinton and LeCun supply the neural and architectural foundations—the “cortex” and reasoning traces—while both Karpathy and their critics note the absence of rich, consolidated memory (the hippocampus analogue), instincts (amygdala), and the capacity for continual, self-motivated world interaction.
Why “Ghosts,” Not “Animals”?
The distinction is not simply philosophical. It carries direct consequences for:
- Capabilities: AI “ghosts” excel at pattern reproduction, simulation, and surface reasoning but lack the embodied, instinctual grounding (spatial navigation, sensorimotor learning) of animals.
- Limitations: They are subject to model collapse, producing uniform, repetitive outputs, lacking the spontaneous creativity and entropy seen in human (particularly child) cognition.
- Future Directions: The field is now oriented towards distilling this cognitive core, seeking a scalable, adaptable reasoning engine—compact, efficient, and resilient to overfitting—rather than continuing to bloat models with ever more static memory.
This lens sharpens expectations: the way forward is not to mimic biology in its totality, but to pursue the unique strengths and affordances of a digital, disembodied intelligence—a spirit of the datasphere, not a beast evolved in the forest.
Broader Significance
Karpathy’s “ghosts” metaphor crystallises a critical moment in the evolution of AI as a discipline. It signals a turning point: the shift from brute-force memorisation of the internet to intelligent, creative algorithms capable of abstraction, reasoning, and adaptation.
This reframing is shaping not only the strategic priorities of the most advanced labs, but also the philosophical and practical questions underpinning the next decade of AI research and deployment. As AI becomes increasingly present in society, understanding its nature—not as an artificial animal, but as a digital ghost—will be essential to harnessing its strengths and mitigating its limitations.
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